FinalReport
Dinos
6/24/2021
Team Members
Onur Oruç 090140323
Sercan Yıldız 090140334
According to World Health Organization (WHO) a pandemic is the worldwide spread of a new disease. Influenza pandemics are unpredictable but recurring events that can significantly affect wealth, communities and economies worldwide. Planning and preparation are critical to help mitigate the risk and impact of a pandemic, and to manage the response and recovery. Governments take cautions against such pandemics by enforcing some policies. These policies show differences in different countries based on their development, population, and economics. There is an ongoing pandemic caused by Covid-19 which have taken many lives. We will be focusing on this pandemic to understand the nature of how government actions can affect the spread of the disease. Then, we will use this information to prevent deaths.
One of the datasets we used in our research shows number of infected and deceased people day by day in different countries between 22nd January 2020 and 15th May 2021 but we used 31st December of 2020 to match with our other dataset. The other dataset we used shows the policies different countries have taken in certain dates.
Link to the first dataset can be found as a csv file from the website: https://ourworldindata.org/policy-responses-covid
Link to the second dataset can be found as a excel file from the website: https://www.acaps.org/covid-19-government-measures-dataset
First, we imported the data from the dataset that shows information about the countries and their daily number of cases.
## [1] "iso_code"
## [2] "continent"
## [3] "location"
## [4] "date"
## [5] "total_cases"
## [6] "new_cases"
## [7] "new_cases_smoothed"
## [8] "total_deaths"
## [9] "new_deaths"
## [10] "new_deaths_smoothed"
## [11] "total_cases_per_million"
## [12] "new_cases_per_million"
## [13] "new_cases_smoothed_per_million"
## [14] "total_deaths_per_million"
## [15] "new_deaths_per_million"
## [16] "new_deaths_smoothed_per_million"
## [17] "reproduction_rate"
## [18] "icu_patients"
## [19] "icu_patients_per_million"
## [20] "hosp_patients"
## [21] "hosp_patients_per_million"
## [22] "weekly_icu_admissions"
## [23] "weekly_icu_admissions_per_million"
## [24] "weekly_hosp_admissions"
## [25] "weekly_hosp_admissions_per_million"
## [26] "new_tests"
## [27] "total_tests"
## [28] "total_tests_per_thousand"
## [29] "new_tests_per_thousand"
## [30] "new_tests_smoothed"
## [31] "new_tests_smoothed_per_thousand"
## [32] "positive_rate"
## [33] "tests_per_case"
## [34] "tests_units"
## [35] "total_vaccinations"
## [36] "people_vaccinated"
## [37] "people_fully_vaccinated"
## [38] "new_vaccinations"
## [39] "new_vaccinations_smoothed"
## [40] "total_vaccinations_per_hundred"
## [41] "people_vaccinated_per_hundred"
## [42] "people_fully_vaccinated_per_hundred"
## [43] "new_vaccinations_smoothed_per_million"
## [44] "stringency_index"
## [45] "population"
## [46] "population_density"
## [47] "median_age"
## [48] "aged_65_older"
## [49] "aged_70_older"
## [50] "gdp_per_capita"
## [51] "extreme_poverty"
## [52] "cardiovasc_death_rate"
## [53] "diabetes_prevalence"
## [54] "female_smokers"
## [55] "male_smokers"
## [56] "handwashing_facilities"
## [57] "hospital_beds_per_thousand"
## [58] "life_expectancy"
## [59] "human_development_index"In this dataset there are 59 columns. But we will be needing the columns “location”, “date”, “new_cases”.
## location date new_cases
## 1 Afghanistan 2020-02-24 1
## 2 Afghanistan 2020-02-25 0
## 3 Afghanistan 2020-02-26 0
## 4 Afghanistan 2020-02-27 0
## 5 Afghanistan 2020-02-28 0
## 6 Afghanistan 2020-02-29 0Our dataset now has 3 columns we need and 89457 rows of information from 180 countries.
Then we imported our second dataset that shows the countries and the measures and the dates they were implented.
## [1] "ID" "ISO" "COUNTRY"
## [4] "REGION" "ADMIN_LEVEL_NAME" "PCODE"
## [7] "LOG_TYPE" "CATEGORY" "MEASURE"
## [10] "TARGETED_POP_GROUP" "COMMENTS" "NON_COMPLIANCE"
## [13] "DATE_IMPLEMENTED" "SOURCE" "SOURCE_TYPE"
## [16] "LINK" "ENTRY_DATE" "Alternative source"In this dataset there are 18 columns. But we will be needing the columns “COUNTRY”, MEASURE“,”COMMENTS“, and”DATE_IMPLEMENTED".
## COUNTRY DATE_IMPLEMENTED
## 1 Afghanistan 2020-01-24
## 2 Afghanistan 2020-01-26
## 3 Afghanistan 2020-01-27
## 4 Afghanistan 2020-01-27
## 5 Afghanistan 2020-02-01
## 6 Afghanistan 2020-02-02
## MEASURE
## 1 Awareness campaigns
## 2 Health screenings in airports and border crossings
## 3 International flights suspension
## 4 Health screenings in airports and border crossings
## 5 Border checks
## 6 Strengthening the public health system
## COMMENTS
## 1 MoPH begins announcements on their facebook to make public aware of coronavirus.
## 2 Health teams at airports will check passengers coming from China.
## 3 Flights to China are suspended.
## 4 Health screenings of all passengers at airports.
## 5 All China and Iran nationals
## 6 the ministry has prepared 100 bed to control this virus in Kabul and 200 others in the province hospital with all the facilities needed in the country.Our dataset now has 4 columns we need and 23923 rows of information from 180 countries.
Then, we added countries from each continent. Some of these countries are the most advanced countries with a large population of the continents they are in. Then, we wanted to add the countries that we are familiar with the policies of and successful at slowing down the Coronavirus. After some discussions we had more than one countries from some continents. In the end, we had 11 countries from different continents. These countries in alphabetical order are:
1 - China
2 - Colombia
3 - Israel
4 - Italy
5 - New Zealand
6 - Nigeria
7 - Norway
8 - Russia
9 - Turkey
10 - United States of America
Since our datasets had different column names for the columns we wanted to merge, we needed to change the column names of the second dataset.
## [1] "location" "date" "MEASURE" "COMMENTS"## [1] "location" "date" "new_cases"Now we have the matching names for country names and dates and it is time to merge them.
## location date new_cases
## 50 Brazil 2020-04-08 2136
## 51 Brazil 2020-04-08 2136
## 52 Brazil 2020-04-08 2136
## 53 Brazil 2020-04-08 2136
## 54 Brazil 2020-04-08 2136
## 55 Brazil 2020-04-08 2136
## 56 Brazil 2020-04-08 2136
## MEASURE
## 50 Domestic travel restrictions
## 51 Domestic travel restrictions
## 52 Limit public gatherings
## 53 Domestic travel restrictions
## 54 Health screenings in airports and border crossings
## 55 Schools closure
## 56 Partial lockdown
## COMMENTS
## 50 In Bahia, the authorities have suspended intercity public transport across the state until 15 April
## 51 flights from Rio de Janeiro and Sao Paulo have been canceled until further notice
## 52 ban on gatherings of 30 or more people, and the cancellation of public events until at least April 30.
## 53 extended reduced public transportation until 3 May.
## 54 ordered health screenings for passengers entering the state by train from Rio de Janeiro and Sao Paulo
## 55 extended until furher notice
## 56 extended a statewide quarantine until at least April 22As it is seen above, we can now work with location, date, new_cases, MEASURE, and COMMENTS columns all together.
Let’s draw map of all countries and their daily number of cases.
It is an interactive map!! You can hold your mouse on lines to see more information and you can disable countries by clicking on them on the legend!!
The first country we will take a look at is Brazil.
First, we wanted to examine Brazil but the number of cases was too unstable. We took a look at their policies anyway to understand what they might have done. But instead of taking any measures against fighting the Coronavirus, Brazilian government opened their borders when they had their peak at the number of Corona cases. Upon further researching, we found out that Brazil is struggling with political crisis. So we can not trust the data we acquire from them. After Brazil, we examined Argentina. But the measures they took also were bad. Argentina and Brazil had similarities in the dates they had peaks and they both opened their borders when they had their peaks. So we moved to another county. And, that’s the one we are going to examine in this study.
08 07 2020 international flights suspended 11 08 2020 partial lockdown 16 08 2020 non essential workplaces closed, stay home 19 08 2020 peak 05 11 2020 can travel to Colombia without test
early January school closure, isolation and quarantine policies, closure of business and public services, partial lockdown 27 01 2020 domestic travel restriction February quick hospitals 26 03 2020 China closed its borders and never opened their borders
15 03 2020 school closure 18 03 2020 border closure 25 03 2020 peak full lockdown 01 08 2020 border opened 07 09 2020 curfews 18 09 2020 partial lockdown, school closure, flights suspended